Auditory attention to natural speech is a complex brain process. Its
quantification from physiological signals can be valuable to improving and
widening the range of applications of current brain-computer-interface systems,
however it remains a challenging task. In this article, we present a dataset of
physiological signals collected from an experiment on auditory attention to
natural speech. In this experiment, auditory stimuli consisting of
reproductions of English sentences in different auditory conditions were
presented to 25 non-native participants, who were asked to transcribe the
sentences. During the experiment, 14 channel electroencephalogram, galvanic
skin response, and photoplethysmogram signals were collected from each
participant. Based on the number of correctly transcribed words, an attention
score was obtained for each auditory stimulus presented to subjects. A strong
correlation ($p<<0.0001$) between the attention score and the auditory
conditions was found. We also formulate four different predictive tasks
involving the collected dataset and develop a feature extraction framework. The
results for each predictive task are obtained using a Support Vector Machine
with spectral features, and are better than chance level. The dataset has been
made publicly available for further research, along with a python library -
phyaat to facilitate the preprocessing, modeling, and reproduction of the
results presented in this paper. The dataset and other resources are shared on
webpage - https://phyaat.github.io.